Self Supervised Loss

Self-supervised learning leverages unlabeled data to train models by employing self-supervised loss functions, aiming to learn robust and generalizable representations without explicit labels. Current research focuses on improving the effectiveness of these losses across diverse modalities (image, text, speech, sensor data), often integrating them with existing architectures like Transformers and Graph Neural Networks, and exploring techniques like contrastive learning and early exiting for efficiency. This approach holds significant promise for various applications, including medical image analysis, remote sensing, and speech processing, by enabling the use of vast amounts of readily available unlabeled data to improve model performance and robustness.